Topics

Featured in Development

As part of our core values of sharing knowledge, the InfoQ editors were keen to capture and share our book and article recommendations for 2018, so that others can benefit from this too. In this second part we are sharing the final batch of recommendations

Featured in Architecture & Design

Tanya Reilly discusses her research into how the fire code evolved in New York and draws on some of the parallels she sees in software. Along the way, she discusses what it means to be an SRE, what effective aspects of the role might look like, and her opinions on what we as an industry should be doing to prevent disasters.

Featured in Culture & Methods

Mik Kersten has published a book, Project to Product, in which he describes a framework for delivering products in the age of software. Drawing on research and experience with many organisations across a wide range of industries, he presents the Flow Framework™ as a way for organisations to adapt their product delivery to the speed of the market.

Featured in DevOps

The fact that machine learning development focuses on hyperparameter tuning and data pipelines does not mean that we need to reinvent the wheel or look for a completely new way. According to Thiago de Faria, DevOps lays a strong foundation: culture change to support experimentation, continuous evaluation, sharing, abstraction layers, observability, and working in products and services.

News

Since Spectre and Meltdown were demonstrated at the beginning of 2018, researchers have been discovering many variants of side-channel vulnerabilities affecting both Intel and AMD CPUs. GPUs seemed instead to be immune to such attacks. Until now, that is.

Microsoft is entering the high-performance computing (HPC) market with their announcement of the general availability of Azure CycleCloud, a tool for creating, managing, operating, and optimizing HPC clusters of any scale in Azure. Furthermore, Microsoft announced it would support NVIDIA GPU Cloud (NGC).

Google announced the general availability of GPUs in their Kubernetes Engine (GKE). Together with the recent GA of 1.10 version of GKE customers can land their machine learning (ML) workloads on to it and leverage the massive processing power of the GPUs.

The new Hybridizer technology provides C# developers with a way to target the CUDA platform and take advantage of GPUs for increased performance. Thanks to Hybridizer, developers are not forced to use C or C++ to write high-performance GPU code.

Today the GPU Technology conference in Munich kicked off with a keynote by NVIDIA CEO Jensen Huang. NVIDIA announced the NVIDIA Holodeck, the Tensor RT 3 library, NVIDIA's Drive platform, and the Pegasus computer for autonomous taxis.

Apple will develop its own custom graphics architecture to power the GPUs for its future devices, according to UK-based firm Imagination Technologies, Apple’s current GPU provider. The new GPUs should be ready in 15 months to two years' time and will be the first Apple-made GPUs that will bear no resemblance to Imagination Technologies’.

Nvidia earlier this month released cuDNN, a set of optimized low-level primitives to boost the processing speed of deep neural networks (DNN) on CUDA compatible GPUs. The company intends to help developers harness the power of graphics processing units for deep learning applications.

The MapReduce paradigm is not always ideal when dealing with large computationally intensive algorithms. A small team of entrepreneurs is building a product called ParallelX to solve that bottleneck by harnessing the power of GPUs to give Hadoop jobs a significant boost.